Deep generative selection models of T and B cell receptor repertoires with soNNia.
Deep generative selection models of T and B cell receptor repertoires with soNNia.
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DOI：
10.1073/pnas.2023141118
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发表时间：
2021-04-06
影响因子：
11.1
通讯作者：
Nourmohammad A
中科院分区：
文献类型：
--
作者：
Isacchini G;Walczak AM;Mora T;Nourmohammad A
适应性免疫系统依赖于许多类型的B和T细胞，其功能反映在其受体序列的不同分子特征上。在这里，我们介绍了一个推理框架，SONIA，它集成了免疫受体生成的可解释的基于知识的模型和灵活而强大的深度学习方法来表征受体功能的序列决定因素。利用SONIA，我们表征了与从不同细胞类型和组织中获得的受体相关的序列特异性选择。我们量化了组成受体的成对链的分子特征之间的协同作用。最后，我们开发了一个基于选择的分类器来识别不同致病表位的特异性T细胞。我们的方法为序列如何决定免疫受体的特定功能提供了一个分子理解。淋巴细胞亚类担负着不同的功能角色，共同工作，产生免疫反应和持久免疫。除了这些功能外，T和B细胞淋巴细胞还依赖于其受体链的多样性来识别不同的病原体。在选择过程中，淋巴细胞亚类来自相同的受体多样性产生的共同祖先。在这里，我们利用受体生成的生物物理模型和机器学习选择模型来识别功能淋巴细胞库和亚库的特定序列特征。具体地说，我们只使用谱系水平的序列信息，对CD4+和CD8+T细胞进行分类，找出在选择过程中出现的受体链之间的相关性，并识别作为致病表位靶标的T细胞亚群。我们还展示了简单的线性分类器以及更复杂的机器学习方法的示例。
The adaptive immune system relies on many types of B and T cells, whose functions are reflected in the distinct molecular features of their receptor sequences. Here, we introduce an inference framework, soNNia, which integrates interpretable knowledge-based models of immune receptor generation with flexible and powerful deep learning approaches to characterize sequence determinants of receptor function. Using soNNia, we characterize sequence-specific selection associated with receptors harvested from different cell types and tissues. We quantify synergetic interactions between the molecular features of the paired chains making up the receptor. Lastly, we develop a selection-based classifier to identify T cells specific to distinct pathogenic epitopes. Our approach provides a molecular understanding for how sequence determines the specific functionality of immune receptors. Subclasses of lymphocytes carry different functional roles to work together and produce an immune response and lasting immunity. Additionally to these functional roles, T and B cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here, we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires and subrepertoires. Specifically, using only repertoire-level sequence information, we classify CD4+ and CD8+ T cells, find correlations between receptor chains arising during selection, and identify T cell subsets that are targets of pathogenic epitopes. We also show examples of when simple linear classifiers do as well as more complex machine learning methods.
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影响因子：
30.8
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